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These overall needs and key services are integrated in RNAVLab (RNA Virtual Laboratory), a virtual laboratory for the study of RNA secondary structures. RNAVLab addresses the challenges above by combining sampling of nucleotide sequences, predictions based on different codes and supported by grid computing technology, and analysis of large sets of secondary structures with different scientific scopes. Scientific scopes include associating stem-loop and pseudoknot types with functions in genomes. In this paper two important case studies using RNAVLab are presented:

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While past efforts have been focused on increasing prediction accuracy of sequential RNA prediction programs [8], [9] and prediction efficiency has been improved by using massively parallel high performance machines [15] or local clusters [16], not much is known about RNA prediction systems based on grid computing technology (i.e., heterogeneous, volatile computers, ranging from supercomputers to clusters and PCs connected to the Internet, spread out in different locations). Previously, grid technology was applied successfully to protein structure prediction [17], [18] and similar achievements are expected for RNA secondary structure prediction. With their significant computing power, these computing systems allow the scientists to explore larger spaces of RNA secondary structures. 2ff7e9595c